{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UT4GV5FK3ML5I2EKFC4Y2C5ZCT","short_pith_number":"pith:UT4GV5FK","schema_version":"1.0","canonical_sha256":"a4f86af4aadb17d4688a28b98d0bb914c5c23a48d0bff7747851c4540f82bba2","source":{"kind":"arxiv","id":"1711.05862","version":1},"attestation_state":"computed","paper":{"title":"Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas K\\\"olsch, Marcus Liwicki, Markus Ebbecke, Muhammad Zeshan Afzal","submitted_at":"2017-11-03T17:02:57Z","abstract_excerpt":"This paper presents an approach for real-time training and testing for document image classification. In production environments, it is crucial to perform accurate and (time-)efficient training. Existing deep learning approaches for classifying documents do not meet these requirements, as they require much time for training and fine-tuning the deep architectures. Motivated from Computer Vision, we propose a two-stage approach. The first stage trains a deep network that works as feature extractor and in the second stage, Extreme Learning Machines (ELMs) are used for classification. The proposed"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1711.05862","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-03T17:02:57Z","cross_cats_sorted":[],"title_canon_sha256":"6cebf533abeaa9caea1d0205589c51d47d72027ca51e1cb6ccc45a49113c95c8","abstract_canon_sha256":"57e1f8d6c7d31ea1c8e8720a6c9b2de059ea7b959432cf0c4d6f47c8c5508037"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:05.638293Z","signature_b64":"LrwZcK31f7RKu8HcUXzJiD4F4i10pFa4ug5XBrtPpzReDOvKst3J/6dK5LiyeRGohFYwZtVsh8YN+beLxn+bDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4f86af4aadb17d4688a28b98d0bb914c5c23a48d0bff7747851c4540f82bba2","last_reissued_at":"2026-05-18T00:20:05.637663Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:05.637663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas K\\\"olsch, Marcus Liwicki, Markus Ebbecke, Muhammad Zeshan Afzal","submitted_at":"2017-11-03T17:02:57Z","abstract_excerpt":"This paper presents an approach for real-time training and testing for document image classification. In production environments, it is crucial to perform accurate and (time-)efficient training. Existing deep learning approaches for classifying documents do not meet these requirements, as they require much time for training and fine-tuning the deep architectures. Motivated from Computer Vision, we propose a two-stage approach. The first stage trains a deep network that works as feature extractor and in the second stage, Extreme Learning Machines (ELMs) are used for classification. The proposed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05862","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.05862","created_at":"2026-05-18T00:20:05.637749+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05862v1","created_at":"2026-05-18T00:20:05.637749+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05862","created_at":"2026-05-18T00:20:05.637749+00:00"},{"alias_kind":"pith_short_12","alias_value":"UT4GV5FK3ML5","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"UT4GV5FK3ML5I2EK","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"UT4GV5FK","created_at":"2026-05-18T12:31:49.984773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT","json":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT.json","graph_json":"https://pith.science/api/pith-number/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/graph.json","events_json":"https://pith.science/api/pith-number/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/events.json","paper":"https://pith.science/paper/UT4GV5FK"},"agent_actions":{"view_html":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT","download_json":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT.json","view_paper":"https://pith.science/paper/UT4GV5FK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05862&json=true","fetch_graph":"https://pith.science/api/pith-number/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/graph.json","fetch_events":"https://pith.science/api/pith-number/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/action/storage_attestation","attest_author":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/action/author_attestation","sign_citation":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/action/citation_signature","submit_replication":"https://pith.science/pith/UT4GV5FK3ML5I2EKFC4Y2C5ZCT/action/replication_record"}},"created_at":"2026-05-18T00:20:05.637749+00:00","updated_at":"2026-05-18T00:20:05.637749+00:00"}